Energy Fluctuations Research Articles

Interstage transmission and differential analysis of pressure fluctuations in multistage centrifugal pump-as-turbine

Pumps as turbines (PATs) are used in petroleum and chemical industries to recover high-pressure residual energy. Multistage PATs allow for a wider energy recovery interval and wider range of applications. However, because multistage centrifugal pumps were not originally designed for turbine conditions, complex pressure fluctuations occur, impacting the stable operation and performance of multistage PAT. Pressure fluctuation is essentially a wave, and by analogy with the wave intensity definition, pressure fluctuations were quantified using the pressure wave energy flow density, and the pressure fluctuation patterns at different stages were investigated. The findings reveal significant differences in the intensity of pressure fluctuations at different locations within the multistage PAT. Specifically, the pressure fluctuation intensity is significantly higher from the second to the final stage, compared to the first stage. The difference in inlet flow conditions is the main reason for this difference in pressure fluctuations between stages. The inlet inflow from the second to the final stage is subject to rotational effects that exacerbate the difference in pressure fluctuation intensity between stages. Pressure fluctuations are found to be negatively related to the distance from the source of fluctuations and positively related to the flow state. Different flow conditions and interaction regions of the impeller affect the distribution of pressure fluctuation intensity and the distribution of pressure fluctuation energy across different frequency domains within the guide vanes. The main source of fluctuations in the shaft frequency and four times the shaft frequency is the impeller inlet interaction region, whereas the fluctuations in the blade passing frequency originate from the impeller outlet interaction region. This paper provides a reference for improving the stable operation of multistage PATs.

Deep cavity noise suppression by exploiting aeroacoustic–structural interaction of multiple elastic panels

This paper reports a numerical study of a novel methodology for passive suppression of deep cavity noise by means of strategically designed and arrangements of multiple elastic panels and examines its underlying aeroacoustic–structural interaction physics. The study is conducted with a freestream, at Mach number 0.09 and Reynolds number of 4 × 104 based on the cavity length, past a two-dimensional cavity by means of direct aeroacoustic simulation coupled with a panel dynamic solver in monolithic fashion. For each cavity-panel configuration, the fluid-loaded panel natural frequencies are harmonized with the characteristic aeroacoustic processes of the original/modified cavity aeroacoustic feedback loop. This promotes panel aeroacoustic-structural resonance for absorption of feedback flow and acoustic fluctuation energy for achieving less eventual cavity noise. The most effective configuration gives a remarkable noise power reduction by 15 dB from a rigid cavity. Inadvertently, it reduces cavity drag by almost 15%. Simultaneous reduction of both cavity noise and drag is unprecedented among similar attempts in the literature. In-depth spatiotemporal analyses of aeroacoustic–structural interaction results elucidate the intricate interplay between cavity flow, panel vibration responses, and cavity acoustic modes, leading to noise reduction in all cavity-panel configurations studied. Essentially, the vertical panel acts to curtail the efficacy of coupling between growing shear layer and cavity acoustic modes whose sustenance is further impeded by an acoustically induced resonant panel at the cavity bottom. The proposed methodology is confirmed to be feasible yet effective, which holds great potential for fluid-moving applications in which a quiet and energy-efficient cavity configuration is desired.

Interactions of adsorbing cosolutes with hydrophobic hydration shells.

The analysis of water density fluctuations in the hydration shell of nonpolar solutes provides insights into water-mediated interactions, especially hydrophobic interactions. These fluctuations are sensitive to small perturbations due to changes in thermodynamic conditions, such as temperature and pressure, but also to the presence of cosolutes, such as salts or small organic molecules. Herein, we investigate the effect of two classes of adsorbing cosolutes, using urea and methanol as representatives, on the fluctuations in energy and solvent density within the solvation shell of a model extended hydrophobic solute. We focus on the interactions of the cosolutes with the hydrophobic hydration shell, rather than with the solute itself, which though important remain largely unexplored. We calculate and analyze the interfacial partial molar energy of the cosolute, using a methodology based on the small system method. This approach provides correlated solvent density and energy fluctuations and allows us to decompose them into contributions due to interactions between the different components present in the solvation shell of the solute. The results show that adsorbed urea molecules interact more favorably with water than nonadsorbed urea molecules, which leads to the attenuation of interfacial density fluctuations and thus to the stabilization of the solvation shell. By contrast, the adsorbed methanol molecules interact preferably with other methanol molecules in the solvation shell, leading to a nano-phase segregated structure, which enhances interfacial fluctuations.

Study of Energy Flow Mechanisms in High Power Device Converters

The work in this paper is applied to the Zhangbei Power grid. In the flexible direct current (DC) power system, the fault current rises extremely fast when a DC fault occurs. The requirements for the peak of breaking current and fault energy absorption of DC circuit breakers (DCCBs) increase linearly, which significantly increases the cost of the equipment. Therefore, in order to reduce the design difficulty of DCCBs, this paper proposes a strategy to control energy after the fault occurs. Firstly, the energy dimension is added on the basis of the traditional vector control of MMC, which constitutes a three-dimensional energy direct control. Subsequently, the architectures of energy fluctuation control and feedforward control are proposed. The influencing mechanisms for the peak fault current, peak fault voltage and energy dissipation are analyzed. Finally, the simulation of energy fluctuation control and feedforward control is constructed on PSCAD/EMTDC. The simulation results show that the energy fluctuation control is obviously better than the conventional three-dimensional energy control, and the feedforward energy control is further improved on this basis. Compared with the conventional vector control, the peak energy is reduced by 45.43% and the peak current is reduced by 25.39%, which helps to simplify the equipment design and reduce the equipment cost.

Solar photothermal utilization of coupled latent heat storage: A numerical and optimization study

The combination of Organic Rankine Cycle (ORC) and Latent Heat Thermal Energy Storage (LHTES) is a novel approach for effectively utilizing solar energy. However, the influence of solar energy fluctuations on the heat transfer process of LHTES systems is not yet clear. In this study, a sinusoidal heat source is applied to a rotating triple-tube LHTES system, and numerical analysis and model verification are conducted. The parameters of the sinusoidal heat source are optimized using the response surface method. The effects of heat source amplitude, half period, and rotation speed on the heat release time and heat release rate of LHTES are investigated, and corresponding quadratic regression equations are fitted. The optimized structure (rotation speed = 0.5 rpm, amplitude = 7.5 K, half-cycle = 100) obtained through the response surface method reduced the solidification time by 12.45% compared to the initial structure (rotation speed = 0.3 rpm, amplitude = 7.5 K, half-cycle = 100). The average temperature response and heat release rate increased by 28.34% and 16.25%, respectively. The sensible heat and total heat release within one solidification cycle increased by 12.17% and 1.87%, respectively. This study lays the foundation for the optimization design of LHTES systems and the integrated application of Organic Rankine Cycle.

Inactivation of polyphenol oxidase by low intensity DC field: Experiment and mechanism analysis via molecular dynamics simulation and molecular docking

In this study, inactivation of mushroom polyphenol oxidase (PPO) by low intensity direct current (DC) electric field and its molecular mechanism were investigated. In the experiments under 3 V/cm, 5 V/cm, 7 V/cm and 9 V/cm electric fields, PPOs were all completely inactivated after different exposure times. Under 1 V/cm, a residual activity of 11.88 % remained. The inactivation kinetics confirms to Weibull model. Under 1–7 V/cm, n value closes to a constant about 1.3. The structural analysis of PPO under 3 V/cm and 5 V/cm by fluorescence emission spectroscopy and molecular dynamics (MD) simulation showed that the tertiary structure was slightly changed with increased radius of gyration, higher potential energy and rate of C-alpha fluctuation. After exposure to the electric field, most of the hydrophobic tryptophan (TRP) residues turned to the hydrophilic surface, resulting the fluorescence red-shifted and quenched. Molecular docking indicated that the receptor binding domain of catechol in PPO was changed. PPO under electric field was MD simulated the first time, revealing the changing mechanism of the electric field itself on PPO, a binuclear copper enzyme, which has a metallic center. All these suggest that the low intensity DC electric field would be a promising option for enzymatic browning inhibition or even enzyme activity inactivation.

Correlated noise enhancement of coherence and fidelity in coupled qubits

ABSTRACT It is generally assumed that environmental noise arising from thermal fluctuations are detrimental to preserving coherence and entanglement in a quantum system. In the simplest sense, dephasing and decoherence are tied to energy fluctuations driven by coupling between the system and the normal modes of the bath. Here, we explore the role of noise correlation in an open-loop model quantum communication system whereby the ‘sender’ and the ‘receiver’ are subject to local environments with various degrees of correlation or anti-correlation. We introduce correlation within the spectral density by solving multidimensional stochastic differential equations and introduce these into the Redfield equations of motion for the system density matrix. We find that correlation can enhance both the fidelity and purity of a maximally entangled (Bell) state. Moreover, by comparing the evolution of different initial Bell states, we show that one can effectively probe the correlation between two local environments. These observations may be useful in the design of high-fidelity quantum gates and communication protocols.

Interconnected Markets: Unveiling Volatility Spillovers in Commodities and Energy Markets through BEKK-GARCH Modelling

Food commodities and energy bills have experienced rapid undulating movements and hikes globally in recent times. This spurred this study to examine the possibility that the shocks that arise from fluctuations of one market spill over to the other and to determine how time-varying the spillovers were across a time. Data were daily frequency (prices of grains and energy products) from 1 July 2019 to 31 December 2022, as quoted in markets. The choice of the period was to capture the COVID pandemic and the Russian–Ukrainian war as events that could impact volatility. The returns were duly calculated using spreadsheets and subjected to ADF stationarity, co-integration, and the full BEKK-GARCH estimation. The results revealed a prolonged association between returns in the energy markets and food commodity market returns. Both markets were found to have volatility persistence individually, and time-varying bidirectional transmission of volatility across the markets was found. No lagged-effects spillover was found from one market to the other. The findings confirm that shocks that emanate from fluctuations in energy markets are impactful on the volatility of prices in food commodity markets and vice versa, but this impact occurs immediately after the shocks arise or on the same day such variation occurs.

Solution for Integration of Renewable Energy Power Plants into Smart Grids with Active Power Control

This article addresses the integration of renewable energy power plants into smart grids and active power control. Renewable energy sources contribute to environmentally friendly and sustainable energy production, but the fluctuations inherent in these sources pose a challenge for energy grids. The article examines various technologies that can be used to overcome this challenge and make energy grids more reliable. Smart grids aim to improve energy grids by optimizing energy production, transmission, and distribution using data analytics, automation, and communication technologies. The integration of renewable energy power plants into these smart grids offers significant advantages, including the ability to predict energy production, integrate with energy storage systems, and manage energy demand. The article also emphasizes the importance of active power control. Active power control is used to manage energy production steadily, thereby maintaining grid stability. Balancing energy fluctuations from renewable energy sources and storing excess energy when needed enhances grid stability. In conclusion, this article discusses the crucial role of integrating renewable energy power plants into smart grids and implementing active power control in the energy sector. These integration and control methods are important steps in making energy grids more sustainable, efficient, and reliable.

The volatility of global energy uncertainty: Renewable alternatives

Fluctuations in energy markets affect all economic activities by affecting energy prices and investment decisions. This study is the first attempt to investigate the energy-related uncertainty (volatility) in the world using the newly developed energy-related uncertainty index (EUI). The ARCH (autoregressive conditional heteroskedasticity) and GARCH (generalized autoregressive conditional heteroskedasticity) models are applied for this investigation between 1996 and 2021. The ARCH model reveals that global energy uncertainty is highly volatile, with a coefficient of 0.63. As policy implications, fostering the transition process to renewable energy and a green economy is recommended. The rise of renewable energy can reduce energy dependency and uncertainty in energy markets since they are less sensitive to exogenous shocks and fossil fuel price fluctuations. Understanding this uncertainty helps policymakers develop more effective energy policies, increase energy security and a sustainable environment, and maintain economic stability. The proposal to increase renewable energy in this study also aligns with the United Nation's SDG7 (Affordable and Clean Energy), which aims to improve the share of renewable energy by 2030.

Improvement of Smart Grid Stability Based on Artificial Intelligence with Fusion Methods

It is crucial to evaluate and anticipate stability under various conditions, as the ability to stabilize a smart grid (SG) is one of its key features for assessing the effectiveness of its design. Intelligent approaches to stability forecasting are necessary to mitigate inadvertent instability in SG design. This is particularly crucial with the expansion of residential and commercial infrastructures, along with the growing integration of renewable energies into these grids. Predicting the stability of SGs is currently a major challenge. The concept of an SG encompasses a broad range of emerging technologies in which artificial intelligence (AI) plays a crucial role and is increasingly being utilized in light of the limitations of conventional methods. It empowers informed decision-making and adaptable responses to fluctuations in customer energy needs, unexpected power outages, rapid changes in renewable energy generation, or any unforeseen crises within an SG system. In this paper, we propose a symmetric approach to enhance SG stability by integrating various machine learning (ML) and deep learning (DL) algorithms, where symmetry is observed in the balanced application of these diverse computational techniques to predict and ensure the grid’s stability. These algorithms utilized a dataset containing the simulation results of the SG stability. The learning phase of these algorithms is based on imprecise and unreliable data. To overcome this limitation, the fusion of classifiers can be a powerful approach to modeling inaccurate and uncertain data, providing more robust and reliable predictions than individual classifiers. Voting and Dempster–Shafer (DS) methods, two commonly used techniques in ensemble learning, were employed and compared. The results show that the use of the fusion of distinct classifiers with voting theory achieves an accuracy of 99.8% and outperforms several other methods including the DS method.

Analysis of Supercapacitors in Renewable Energy Systems for Managing Power Fluctuations

Environmental decarbonization drives the world to find better ways to generate and store energy. Sustainable energy in the form of solar and wind is explored with the use of conventional energy storage systems (batteries) to close the gap. Green energy generation is weather-dependent, leading to power output fluctuations, and the short-term variability in irradiance adversely affects the system’s energy output and reliability. Standalone operation of a generating system necessitates a storage energy unit that manages transient loading and effectively shares power between the load and energy storage. This article presents an approach to managing energy fluctuations when renewable energy sources fluctuate, this occurs when short-term variability in irradiance, and transient loading occurs. The approach uses supercapacitors as a short-term energy storage solution. The proposed configuration has the following key advantages: effective power sharing, rapid charge, and discharge cycles in supercapacitors result in voltage restoration under transient conditions. The performance of the system is confirmed in MATLAB/Simulink. The proposed study confirms that integrating supercapacitors with PV systems does provide significant power oscillation management when sudden variations occur in renewable sources. A practical test was performed using smaller components following the same concept, and the results supported the hypothesis. The results show that the output voltage is managed when oscillation in irradiance occurs. This is seen in the simulation without supercapacitors when the output voltage fluctuates between 100 and 450 V within the first second of the simulation; however, with the introduction of SC, the voltage becomes stable and maintains an output of 620 V within 0.02 seconds. This is further supported by the experimental outcomes where almost 50% of the solar panel is covered and the output voltage is maintained.

Global perspectives on bipolar disorder treatment: in-depth comparative analysis of international guidelines for medication selection.

Bipolar disorder, a chronic mental health condition characterised by fluctuations in mood, energy and functionality, affects millions of individuals worldwide. Its management requires a comprehensive approach, and, as such, treatment guidelines have a pivotal role in guiding clinicians to alleviate symptoms, prevent relapse and enhance overall patient well-being. However, the treatment landscape is far from homogenous, with significant variations existing across different countries. This study aimed to explore and compare treatment guidelines for bipolar disorder in various regions, shedding light on the factors that influence therapeutic approaches and thus offering insights that could contribute to the ongoing refinement of evidence-based practices in management. The study explores various international treatment guidelines for bipolar disorder that have been updated after 2014. Guidelines from the UK, Canada, Australia/New Zealand, South Korea and the International College of Neuropsychopharmacology are scrutinised to identify factors contributing to the observed differences among them. The variations in recommended drugs across guidelines arise from the approaches employed in guideline development - whether relying on expert consensus or meta-analysis results. Timing disparities in conducting these analyses and the selection of studies also exert influence. Moreover, differences in metabolic enzymes among diverse races and the health policies implemented by individual nations play a significant part in shaping these differences. The primary hindrance to consistent treatment conclusions lies in the scarcity of high-quality research results, leading to variations in guidelines. Enhancing evidence-based recommendations necessitates the undertaking of large-scale studies dedicated to assessing treatments for bipolar disorder.

Evolution of interfacial phases between Al alloy and high entropy alloy during annealing

While the interfacial phases between Al and high-entropy alloy (HEA) share the same types as that between Al and steel, it has been found previously that the thermal stability of the Al-HEA interfacial phases is significantly higher than that of the Al-steel interfacial phases. To further elucidate the reason for the exceptional thermal stability of Al-HEA interfacial phases, this study investigated the evolution of the interfacial phases formed between Al alloy and FeCoCrNiMn HEA during annealing. An Al-FeCoCrNiMn thin film diffusion couple was extracted from a friction stir lap welding (FSLW) joint by focused ion beam and then annealed at 400 °C. An amorphous layer is formed initially at the interface due to the high strain rate deformation during FSLW. The amorphous layer remained even after prolonged annealing at 400 °C (∼0.72 Tm of Al alloy) for 60 min with very limited thickening (from 50 nm to 55 nm), showing remarkable thermal stability at the relatively high temperature. Al13Fe4-type intermetallic compound (IMC) with the Fe site occupied by Fe, Co, Cr, Ni and Mn first nucleates at the interface between Al alloy and amorphous layer. With the annealing time extended to 180 min, the amorphous layer is partially transformed into Al5Fe2-type IMC. The rate constant for the thickening of the reaction layer at the Al/FeCoCrNiMn interface is 0.03 μm∙min1/2, which is much smaller than (only 0.02 times) that at the Al/steel interface under similar annealing condition. The high thermal stability of Al-HEA interfacial layer is thus attributed to the slow kinetics in crystallization of the initially formed amorphous layer, the barrier effect of amorphous layer for interdiffusion, and the reduced diffusivity inside of IMCs caused by significant fluctuations in lattice potential energy due to high-density doping with HEA elements. This finding provides new insights into the fabrication of thermally stable Al-HEA hybrid structures suitable for service conditions involving high-temperature exposure.

Study on Wear Characteristics of a Guide Vane Centrifugal Pump Based on CFD–DEM

Guide vane submersible centrifugal pumps are a kind of submersible pump, and the fluid inside the pump is often mixed with gravel and other impurities during operation, affecting the pump’s operating efficiency and life expectancy. However, past studies on solid–liquid two-phase flow (STF) and wear characteristics in guided vane centrifugal pumps have been limited to the particle trajectory and wear region distribution. These studies have lacked research on the effect of particles on the fluid flow and the specific amount of wear on the overflow components. Additionally, most of them have used the DPM discrete-phase model, which does not consider the particle–particle and particle–wall interactions. This paper is based on the CFD–DEM method, combined with the Archard wear model. A solid–liquid two-phase flow simulation is carried out for pumps with different particle sizes and particle shapes to analyze the particle movement inside the pump, the wear distribution and average wear amount of the overflow components, and the effect of particles on the turbulent kinetic energy of the fluid. The results show that the particles mainly collide with the leading and trailing edges of the impeller blades and the leading edge of the guide vane blades and form a buildup at the trailing edge of the concave surface of the guide vane blades, resulting in the wear being mainly distributed in these regions. With an increase in particle size and a decrease in sphericity, the average wear on the overflow components increases. The change of particle size directly affects the resistance of the fluid and the structure of the flow field, which has a large impact on the fluid flow pattern and generates large turbulent kinetic energy fluctuations. The shape of the particles only changes the structure of the local flow field, which has a small impact on the fluid flow pattern.

Molecular dynamics study on relaxation of supercooled liquid water at different cooling rates

The relaxation in supercooled liquids is a phenomena by which the thermodynamic system starts transforming from one state to other after losing its high end thermodynamic properties (i.e. density, potential energy, enthalpy, etc.) with a significant change. Since the relaxation phenomena is quite challenging to observe, specifically near the transition region of liquid water. Hence, the thermodynamic analysis for understanding the relaxation phenomena of liquid water in supercooled region from the high density liquid (HDL) state to low density amorphous (LDA) state are important from both fundamental and as well as applied perspectives. Therefore, we focused our study on performing thermodynamic analysis on the relaxation of super-cooled liquid water by using a monatomic water (mW) potential model with an isothermal-isobaric (NPT) molecular dynamics (MD) simulation technique at zero pressure under different cooling rates. In this work, we examine the relaxation in terms of root mean square (RMS) fluctuation of two and three-body potential energy of the maximum size of four coordinated cluster under different cooling rates. The predicted data over this study shows the independency of the cooling rate, where the relaxation of the liquid state of mW water results in a sharp irreversible drop in potential energy and large fluctuation in two and three body potential energy of the maximum size of four coordinated cluster. Further, we found the relaxation of liquid state of mW water associated with the sharp and continuous increase in the fraction of four coordinated particles as a function of temperature for different cooling rates. The predicted results of this study are consistent with the literature, which helps to understand the relaxation phenomena of mW water and other such thermodynamic systems more significantly.

Standalone hybrid power-hydrogen system incorporating daily-seasonal green hydrogen storage and hydrogen refueling station

In light of the recent developments in the green hydrogen industry, it is essential to investigate its compatibility and integration with existing electrical grids. This task becomes more challenging when considering its implementation in off-grid sites that solely rely on renewable energy. In cases where off-grid sites should provide electricity and hydrogen to a variety of consumers, it is compulsory to have electrical and hydrogen storage devices with long-term storage capabilities. Consequently, a precise programming technique is necessary to manage the storage and consumption of hydrogen and electricity. To investigate such a model, this paper presents a connected electricity-hydrogen grid in which all the required energy is supplied by solar and hydroelectric units. This off-grid system supplies the necessary energy for industrial and residential loads, electric vehicle charging stations, fuelcell car refueling stations, and natural gas pipelines. The power-to-hydrogen (P2H) and hydrogen-to-power (H2P) systems are used to facilitate energy flow between the electrical and hydrogen sectors. A daily-seasonal hydrogen storage is integrated as well. The proposed method contributes by integrating and improving the modeling, management, optimization, and operation of the daily-seasonal hydrogen storage and electrical storage. Its primary objective is to supply electricity and hydrogen to various consumers using solely renewable energy, even in the face of renewable energy fluctuations and outages. The model is mathematically expressed as a mixed integer linear optimization, implemented in GAMS software, and solved by the CPLEX solver. It aims at maximizing profit from selling electricity and hydrogen to the users. Results demonstrate that P2H and H2P units have a complementary operation with the solar system. When there is abundant solar energy, the P2H converts excess clean energy to green hydrogen and H2P operates when solar energy is not available. Seasonal storage also stores hydrogen from spring to autumn and discharges it in winter because of higher hydrogen prices in this season. Such an operation increases the annual profit by about 9%. When the hydroelectricity unit faces an outage in hours 8 to 13, the fuelcell uses the stored hydrogen and comes into operation to compensate hydropower outage. The net present value of profit amounts to $275,179 per year. The total hydrogen required is approximately 49,870 kg per year, with a total electricity requirement of 1190 MWh per year. The levelized revenue from hydrogen is achieved at around $4 per kilogram, while the levelized revenue from electricity is about $0.058 per kilowatt-hour. It is concluded that the integration of seasonal storage effectively reduces costs, boosts profits, and efficiently manages renewable energy fluctuations across different seasons.

Neural network predictive control in renewable systems (HKT-PV) for delivered power smoothing

The reduction of power fluctuations from intermittent renewable sources is one of the most pressing challenges today. Recent research has shown that prediction and control mechanisms, when combined with energy storage systems, significantly contribute to improving these techniques. However, substantial research gaps still exist regarding the optimization of energy storage system operability. This article introduces an innovative power smoothing method based on neural network predictive control, in conjunction with the exponential moving average method. The proposed approach encompasses the ability to substantially reduce energy fluctuations, optimize battery state of charge, and mitigate ramp rates, thereby preventing deep discharges that shorten battery lifespan. Furthermore, the control system's primary objective is to optimize energy exchange with the grid, surpassing the performance offered by other conventional power smoothing methods. The control system excels in optimizing energy exchange within the network, surpassing conventional methods. Extensive testing on the University of Cuenca microgrid reveals a consistently more stable and higher battery charge compared to conventional methods. Numerical results for underscore the method's effectiveness with a fluctuation suppression rate of 30.78 % compared to 34.85 % (low pass filter) and 36.22 % (ramp rate) methods respectively. The enhanced voltage profiles at the common coupling point ensure the delivery of high-quality and stable power.

Enhancing process state monitoring in energy storage systems: A robust design synthesis with physics-informed generalized observer

Energy storage systems (ESSs) are crucial for managing renewable energy fluctuations. Knowing ESSs’ states is vital for thermal management. This paper presents a robust design synthesis approach, leveraging a physics-informed generalized observer (GO), for enhancing the process state monitoring in ESSs. The proposed GO takes advantage of the inherent characteristics of ESSs as interconnected subsystems with distinct physical significance. By utilizing measured inputs and outputs, the GO accurately estimates the unmeasurable and unknown process states. GO employs a physics-informed model and a tracking feedback correction mechanism for accurate state estimation. It offers high accuracy, robustness, and insensitivity to measurement noise. To validate the effectiveness of the proposed approach, two case studies are conducted on ESSs. The first involves monitoring the core temperature of lithium batteries, and the second involves an experimental study on monitoring the inner chamber temperature of heat buffer tanks. Experimental results demonstrate approximately 10% improvement compared to conventional methods. GO provides a simple design method, clear physical interpretation, and strong interpretability. Overall, GO is an effective and versatile observer for process monitoring with significant advantages and potential for various applications.

Generalized Gaussian states in group field theory and su(1,1) quantum cosmology

We define generalized Gaussian states for quantum cosmological models based on the su(1,1) algebra, with particular emphasis on its realization in group field theory for a single field mode, and study their semiclassical properties. These states are generalizations of coherent, squeezed, and thermal states considered previously. As two possible characterizations of semiclassicality, we contrast the requirement of small relative fluctuations in volume and energy with the saturation of the Robertson-Schrödinger uncertainty principle. We find that for the most general class of states, the appearance of small relative fluctuations, which we take as the main criterion relevant for the emergence of cosmology, is mostly determined by the amount of displacement used to define the state. We also observe that defining such generalized Gaussian states is less straightforward in the algebraic approach to canonical quantization of group field theory, and discuss special cases. Published by the American Physical Society 2024